Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions Prediction

Maria Elena Garcia Ochagavia (), Yudivian Almeida Cruz (), Suilan Estevez Velarde (), Aimee Alonso Reina () and Maria Elena Ochagavia Roque ()
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Maria Elena Garcia Ochagavia: Universidad de La Habana
Yudivian Almeida Cruz: Universidad de La Habana
Suilan Estevez Velarde: Universidad de La Habana
Aimee Alonso Reina: Universidad de La Habana
Maria Elena Ochagavia Roque: Centro de Ingenieria Genetica y Biotecnologia

Revista Internacional de Gestión del Conocimiento y la Tecnología (GECONTEC), 2019, vol. 7, issue 2, 80-96

Abstract: Developing a drug from scratch is a very long and expensive process that has a small probability of success. For this reason, pharmaceutical companies are devoting their efforts to find drugs that could be repositioned. When using a drug to treat a disease is necessary to consider what adverse reactions it may cause, this is why the prediction of adverse reactions is highly related to drug repositioning. We propose the detection of overlapping communities over a biological network of chemicals, diseases and genes in order to find drug-disease pairs that could be used as basis for later drug repositioning and adverse reactions prediction analysis. Of the evaluated overlapping community detection algorithms, OSLOM got the best results, producing 724 communities from which was possible to extract 215944 drug-disease pairs not present in the analyzed graph. We illustrate the usefulness of this set through examples of associations between pairs found in the scientific literature.

Keywords: Drug repositioning; adverse reactions; overlapping community detection; biological network (search for similar items in EconPapers)
JEL-codes: D8 D81 M1 M15 O3 O31 O32 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:rge:journl:v:7:y:2019:i:2:p:80-96

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